The long-term goal of this proposed research is to identify plasma and/or urine biomarkers of chronic kidney disease (CKD) that provide better diagnostic, mechanistic, and prognostic information than current indices of kidney function. Catalyzed by recent advances in analytical chemistry, coupled with computational power, this proposal integrates metabolite profiling with access to well-characterized clinical cohorts to address a widely recognized need for improved biomarkers of CKD. Small molecule metabolites are promising biomarkers in this context for several reasons: 1) CKD is a condition defined by aberrant filtration, re-absorption, and excretion of small molecule metabolites;2) CKD is at the intersection of various metabolic disease pathways, including hypertension, insulin resistance, dyslipidemia, and inflammation: circulating metabolites may themselves participate as regulatory signals, as in the control of blood pressure, energy homeostasis, and leukocyte recruitment;and 3) High throughput metabolite profiling is practical and feasible with current technology. We hypothesize that individuals with CKD have distinct plasma and urine metabolites profile as compared to normals and that a subset of metabolites associated with CKD will predict CKD progression. Our hypothesis will be tested by the following specific aims: Aim 1. To use a targeted, LC-MS/MS-based metabolomics platform to discover novel plasma and urine biomarkers of CKD in subjects with CKD stages 1-5 with both diabetic and non-diabetic nephropathy. Aim 2. To validate novel plasma and urine biomarkers of CKD in a prospective cohort of CKD subjects by verifying their quantitative and qualitative association with CKD at baseline (Aim 2A), and demonstrating their ability to predict CKD progression over time (Aim 2B). Independent of these aims, we believe these initial efforts to profile plasma and urine across a spectrum of CKD severity and etiology will be a valuable contribution to ongoing efforts to annotate the human metabolome, and will inform future metabolomics studies of human disease. Current markers of kidney disease fail to reliably detect early kidney disease and are unable to predict which individuals will have worsening kidney function. New technologies now allow for rapid and broad screening for new markers of kidney disease. Better markers of kidney disease that permit earlier diagnosis would allow earlier treatment and planning, and would help efforts to discover new treatments for kidney disease.